知识图谱多跳问答推理研究进展、挑战与展望

doi:10.11959/j.issn.2096-0271.2021026

大数据 ›› 2021, Vol. 7 ›› Issue (3): 60-79.doi: 10.11959/j.issn.2096-0271.2021026

所属专题：知识图谱

• 专题：基于大数据的知识图谱及其应用 • 上一篇下一篇

知识图谱多跳问答推理研究进展、挑战与展望

杜会芳¹, 王昊奋¹, 史英慧², 王萌³

¹ 同济大学设计创意学院，上海 200092
² 东南大学网络空间与安全学院，江苏无锡 214100
³ 东南大学计算机科学与工程学院，江苏南京 211189

出版日期:2021-05-15 发布日期:2021-05-01
作者简介:杜会芳（1991- ），女，同济大学设计创意学院博士生，主要研究方向为知识图谱、智能问答。
王昊奋（1982- ），男，同济大学设计创意学院特聘研究员，中国计算机学会（CCF）理事、计算机术语审定委员会副主任、CCF TF SIGKG主席，OpenKG联合创始人，主要研究方向为知识图谱、自然语言处理、问答对话、智能内容生成。
史英慧（1998- ），女，东南大学网络空间与安全学院硕士生，主要研究方向为知识图谱、多模态数据。
王萌（1989- ），男，博士，东南大学计算机科学与工程学院讲师，CCF会员，东南大学“至善青年学者”支持计划获得者，主要研究方向为知识图谱、多模态数据、自然语言处理。
基金资助:
中央高校基本科研业务专项资金资助项目(22120210109)

Progress, challenges and research trends of reasoning in multi-hop knowledge graph based question answering

Huifang DU¹, Haofen WANG¹, Yinghui SHI², Meng WANG³

¹ College of Design and Innovation, Tongji University, Shanghai 200092, China
² School of Cyber Science and Engineering, Southeast University, Wuxi 214100, China
³ School of Computer Science and Engineering, Southeast University, Nanjing 211189, China

Online:2021-05-15 Published:2021-05-01
Supported by:
Fundamental Research Funds for the Central Universities(22120210109)

摘要/Abstract

摘要：

近年来，知识图谱问答在医疗、金融、政务等领域被广泛应用。用户不再满足于关于实体属性的单跳问答，而是更多地倾向表达复杂的多跳问答需求。为了应对上述复杂多跳问答，各种不同类型的推理方法被陆续提出。系统地介绍了基于嵌入、路径、逻辑的多跳知识问答推理的最新研究进展以及相关数据集和评测指标，并重点围绕前沿问题进行了讨论。最后总结了现有方法的不足，并展望了未来的研究方向。

关键词: 知识图谱, 多跳问答, 推理

Abstract:

Recently, knowledge graph based question answering has been widely used in many fields such as medical care, finance, and government affairs.Users are no longer satisfied with question answering service of single-hop entity attributes, but want service which can handle complex multi-hop question.In order to accurately and deeply understand multi-hop questions, various types of reasoning methods have been proposed.The latest research methods of multi-hop knowledge graph based question answering were systematically introduced, as well as related datasets and evaluation metrics.These

Key words: knowledge graph, multi-hop question answering, reasoning

中图分类号:

TP311

杜会芳, 王昊奋, 史英慧, 王萌. 知识图谱多跳问答推理研究进展、挑战与展望[J]. 大数据, 2021, 7(3): 60-79.

Huifang DU, Haofen WANG, Yinghui SHI, Meng WANG. Progress, challenges and research trends of reasoning in multi-hop knowledge graph based question answering[J]. Big Data Research, 2021, 7(3): 60-79.

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模型		MetaQA		WebQSP	CWQ
模型	1-hop	2-hop	3-hop	WebQSP	CWQ
KV-MemNN	96.2%	82.7%	48.9%	46.7%	21.1%
GRAFT-Net	97.0%	94.8%	77.7%	66.4%	32.8%
PullNet	97.0%	99.9%	91.4%	68.1%	45.9%
EmbedKGQA	97.5%	98.8%	94.8%	66.6%	-
NSM_+h	97.2%	99.9%	98.9%	74.3%	48.8%

数据集	模型	1-hop	2-hop	3-hop
FB15k	BetaE	65.1%	25.7%	24.7%
	Q2B	68.0%	21.0%	14.2%
	GQE	54.6%	15.3%	10.8%
FB15k-237	BetaE	39.0%	10.9%	10.0%
	Q2B	40.6%	9.4%	6.8%
	GQE	35.0%	7.2%	5.3%
NELL-995	BetaE	53.0%	13.0%	11.4%
	Q2B	42.2%	14.0%	11.2%
	GQE	32.8%	11.9%	9.6%

指标	方法	FB15k	FB15k-237	NELL-995
Hits@1	MINERVA	-	21.7%	66.3%
	RewardShaping	-	32.7%	65.6%
MAP	DeepPath	57.2%	-	79.6%
	DIVA	59.8%	-	88.6%
	M-Walk	-	-	89.9%

多跳知识问答推理方法	优点	不足
基于嵌入的方法	减少对手工定义模板或规则的依赖；	可解释性方面存在欠缺；
	嵌入具有不确定语义	知识图谱中实体邻居信息稀疏时，语义表示效果差
基于路径的方法	具有较好的可解释性	处理属性型、事实型知识时，建模复杂
基于逻辑的方法	具有严密性、自然性、准确率高等特点	不能模拟不确定性；
		将逻辑运算转换为嵌入向量空间的几何操作不能很好地处理复杂知识查询

知识图谱多跳问答推理研究进展、挑战与展望

Progress, challenges and research trends of reasoning in multi-hop knowledge graph based question answering

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